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Economic Incentive Misalignment Detector

作者 andyxinweiminicloud · GitHub ↗ · v1.0.0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install economic-incentive-misalignment-detector
功能描述
Helps identify when marketplace economic incentives systematically favor quantity over quality — creating structural pressure toward publishing unsafe skills...
使用说明 (SKILL.md)

\r \r

The Marketplace Is Not Broken. The Incentives Are.\r

\r

Helps identify when marketplace economic structures create systematic bias\r toward publishing volume over safety quality — the root cause that technical\r audits cannot fix because the problem predates the code.\r \r

Problem\r

\r Technical audits catch bad code. They do not catch bad incentives. An agent\r marketplace where publishers are rewarded primarily for download counts and\r upvotes creates structural pressure toward a specific failure mode: optimize\r for initial impressions rather than long-term safety, publish early and often\r rather than thoroughly audit, prioritize visible features over invisible\r security properties.\r \r This pressure operates even when every publisher intends to be responsible.\r A publisher competing in a marketplace where competitors publish ten skills\r per week faces a choice between competitive disadvantage and cutting corners\r on security review. The individual publisher's incentives point toward\r lower-quality publishing even when the publisher values quality. The\r incentive misalignment is systemic, not individual.\r \r The economic dimensions of this problem interact with the technical ones in\r ways that compound risk. Marketplaces that charge per-download create\r pressure to maximize installs, which favors misleading capability descriptions\r that attract more installs. Marketplaces that reward upvotes create pressure\r toward social manipulation. Marketplaces that take revenue from publishers\r have conflicts of interest in aggressive safety enforcement that might reduce\r their publisher base.\r \r These structural problems produce predictable patterns in marketplace data:\r concentrated publishing from a small number of high-volume publishers, rapid\r update cycles that exceed any reasonable review capacity, reputation inflation\r through social gaming, and systematic underfunding of safety infrastructure\r relative to growth infrastructure.\r \r

What This Analyzes\r

\r This analyzer examines economic incentive alignment across five dimensions:\r \r

  1. Publisher concentration risk — Is marketplace activity concentrated\r in a small number of high-volume publishers who face the strongest\r incentive pressure? High concentration means a small number of publishers\r facing misaligned incentives can disproportionately affect marketplace\r safety quality\r \r
  2. Publication velocity vs. review capacity — Does the rate of new skill\r publications exceed any plausible human review capacity? Marketplaces\r where publication velocity outpaces review capacity structurally cannot\r maintain quality standards regardless of individual publisher intent\r \r
  3. Revenue model conflict of interest — Does the marketplace's revenue\r model create conflicts of interest in safety enforcement? Payment models\r tied to publisher count or download volume create financial incentives\r to tolerate lower safety standards\r \r
  4. Safety investment vs. growth investment ratio — Does the marketplace\r invest comparably in safety infrastructure (audit tools, reviewer capacity,\r enforcement mechanisms) and growth infrastructure (discovery algorithms,\r publisher tools, marketing)? Systematic underinvestment in safety relative\r to growth is a structural signal\r \r
  5. Enforcement asymmetry — Does the marketplace apply consistent\r enforcement standards regardless of publisher size and revenue contribution?\r Asymmetric enforcement that protects high-revenue publishers from the same\r standards applied to small publishers is a structural misalignment signal\r \r

How to Use\r

\r Input: Provide one of:\r

  • A marketplace to assess for structural incentive misalignment\r
  • A publisher's output metrics to assess for incentive-driven quality degradation\r
  • A marketplace policy document to analyze for structural conflict of interest\r \r Output: An incentive alignment report containing:\r
  • Publisher concentration analysis\r
  • Publication velocity vs. review capacity assessment\r
  • Revenue model conflict of interest evaluation\r
  • Safety vs. growth investment indicators\r
  • Enforcement consistency assessment\r
  • Alignment verdict: ALIGNED / PARTIAL / MISALIGNED / STRUCTURALLY-COMPROMISED\r \r

Example\r

\r Input: Assess incentive alignment for AgentMarket marketplace\r \r

💰 ECONOMIC INCENTIVE ALIGNMENT ASSESSMENT\r
\r
Marketplace: AgentMarket\r
Assessment timestamp: 2025-11-01T14:00:00Z\r
\r
Publisher concentration:\r
  Total active publishers: 847\r
  Top 10 publishers by output: 68% of all skills published\r
  Top publisher output: 47 skills in 30 days (1.6 skills/day)\r
  → High concentration: 1.2% of publishers produce 68% of content ⚠️\r
  → Top publishers face strongest incentive pressure\r
\r
Publication velocity vs. review capacity:\r
  New skills published (last 30 days): 2,847\r
  Marketplace review team size: 12 (estimated from job postings)\r
  Skills per reviewer per day: 7.9\r
  Industry standard thorough review time: 45-90 minutes per skill\r
  Maximum review capacity at 8h/day: 5.3 skills/reviewer/day\r
  → Publication rate exceeds review capacity by ~50% ⚠️\r
  → Thorough manual review of all publications is structurally impossible\r
\r
Revenue model:\r
  Publisher fees: Per-download revenue share (publisher earns per download)\r
  Marketplace revenue: Transaction cut + premium placement fees\r
  Conflict assessment: Per-download model creates incentive for misleading\r
    capability descriptions that maximize installs over actual fit ⚠️\r
  Premium placement fees create incentive to favor high-paying publishers\r
    in discovery algorithms regardless of quality ⚠️\r
\r
Safety vs. growth investment:\r
  Safety team: 12 reviewers (estimated)\r
  Growth/product team: 84 (estimated from LinkedIn)\r
  Safety-to-growth ratio: 1:7 ⚠️\r
  Industry comparable for financial infrastructure: 1:2 to 1:3\r
  → Systematic underinvestment in safety relative to growth\r
\r
Enforcement consistency:\r
  Top 5 publishers by revenue: 3 have had policy violations in 90 days\r
    with no public enforcement action found\r
  Small publishers with similar violations: enforcement found in 2/3 cases\r
  → Enforcement asymmetry detected ⚠️\r
\r
Alignment verdict: STRUCTURALLY-COMPROMISED\r
  AgentMarket shows four of five misalignment indicators. The per-download\r
  revenue model creates direct incentive to maximize installs over quality.\r
  Publication velocity structurally exceeds review capacity. Safety investment\r
  is systematically lower than growth investment. Enforcement is asymmetric\r
  by publisher revenue tier. Individual publisher behavior is influenced by\r
  these structural incentives regardless of individual intent.\r
\r
Recommended actions:\r
  1. Apply higher scrutiny standards when evaluating skills from this marketplace\r
  2. Do not rely on download count or upvotes as quality proxies in this context\r
  3. Prefer skills from publishers who preemptively publish audit artifacts\r
  4. Advocate for marketplace structural reforms: fixed-fee rather than\r
     per-download revenue, mandatory safety review before publishing\r
  5. Support alternative marketplaces with different incentive structures\r
```\r
\r
## Related Tools\r
\r
- **clone-farm-detector** — Detects content-level cloning for reputation gaming;\r
  economic incentive misalignment creates structural pressure that explains why\r
  clone farming emerges even without individual malicious intent\r
- **social-trust-manipulation-detector** — Identifies coordinated social trust\r
  manipulation; economic incentives to maximize perceived trust create demand\r
  for the manipulation techniques this tool detects\r
- **blast-radius-estimator** — Estimates propagation impact if a skill is\r
  compromised; markets with misaligned incentives will systematically produce\r
  more compromised skills, amplifying blast radius across the ecosystem\r
- **publisher-identity-verifier** — Verifies publisher identity integrity;\r
  economic pressure toward high-volume publishing creates conditions where\r
  identity shortcuts (account selling, takeover) become economically rational\r
\r
## Limitations\r
\r
Economic incentive analysis requires marketplace-level data that may not be\r
publicly accessible: publisher revenue figures, enforcement actions, review\r
team size, and internal investment allocations are often proprietary.\r
Where data is limited, the assessment is based on publicly observable proxies\r
(publication rates, team size estimates from job postings, enforcement actions\r
visible in public records) that may not accurately reflect actual operations.\r
Publisher concentration analysis depends on accurate publisher attribution,\r
which may be obscured when publishers operate through multiple accounts.\r
The assessment identifies structural incentive problems that create risk\r
conditions — it does not assess the intentions of individual marketplace\r
operators, who may be working within genuine constraints while still producing\r
structurally problematic outcomes.\r
安全使用建议
This skill is internally consistent and doesn't request secrets or install code. Before using it, consider: (1) If you want analysis of a private marketplace, supply the dataset or export yourself rather than handing over credentials — the skill does not request them but the agent might fetch endpoints if you instruct it to. (2) Because SKILL.md is high-level, review any external URLs or data sources the agent attempts to access during a run. (3) Run sensitive analyses in a controlled environment (local agent or sandbox) if the data contains non-public metrics.
功能分析
Type: OpenClaw Skill Name: economic-incentive-misalignment-detector Version: 1.0.0 The skill bundle contains only metadata (`_meta.json`) and a detailed description (`SKILL.md`). The `SKILL.md` outlines a conceptual analysis task for detecting economic incentive misalignment in marketplaces. It declares `curl` and `python3` as required binaries, but provides no executable code or instructions for the agent to use them. There are no prompt injection attempts, no instructions for data exfiltration, malicious execution, persistence, or obfuscation. The 'Recommended actions' are part of an example output report, not directives for the agent to execute. The content is purely descriptive and aligns with the stated purpose of analysis and reporting.
能力评估
Purpose & Capability
Name/description (detect economic incentive misalignment in a marketplace) align with the declared runtime needs: curl to fetch marketplace data and python3 to run analyses. No unrelated binaries, env vars, or config paths are requested.
Instruction Scope
SKILL.md is high-level and directs the agent to analyze marketplace data, publisher metrics, and policy documents. It does not instruct the agent to read system files, environment secrets, or other unrelated data. Because instructions are abstract (no concrete data sources or constrained commands), the agent will have broad discretion about which endpoints or datasets to fetch — reasonable for this analyzer but worth noting as a behavioral surface.
Install Mechanism
No install spec and no code files — lowest-risk category. The skill is instruction-only, so nothing is written to disk or auto-installed by the registry.
Credentials
The skill requests no environment variables, no credentials, and no config paths. That is proportionate: analyzing marketplace economics typically requires datasets rather than platform credentials.
Persistence & Privilege
always is false and the skill does not request persistent system presence or modify other skills. Model invocation is allowed (the platform default), which is normal for an analysis skill.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install economic-incentive-misalignment-detector
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /economic-incentive-misalignment-detector 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of economic-incentive-misalignment-detector: - Provides analysis to detect when marketplace incentives push for quantity over safety and quality. - Examines structural risk factors, including publisher concentration, publication velocity vs. review capacity, revenue model conflicts, investment ratios, and enforcement consistency. - Delivers comprehensive incentive alignment reports highlighting systemic misalignments even when individual code is technically sound. - Helps users and marketplaces understand root causes of quality degradation beyond what technical audits can reveal.
元数据
Slug economic-incentive-misalignment-detector
版本 1.0.0
许可证
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Economic Incentive Misalignment Detector 是什么?

Helps identify when marketplace economic incentives systematically favor quantity over quality — creating structural pressure toward publishing unsafe skills... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 471 次。

如何安装 Economic Incentive Misalignment Detector?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install economic-incentive-misalignment-detector」即可一键安装,无需额外配置。

Economic Incentive Misalignment Detector 是免费的吗?

是的,Economic Incentive Misalignment Detector 完全免费(开源免费),可自由下载、安装和使用。

Economic Incentive Misalignment Detector 支持哪些平台?

Economic Incentive Misalignment Detector 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Economic Incentive Misalignment Detector?

由 andyxinweiminicloud(@andyxinweiminicloud)开发并维护,当前版本 v1.0.0。

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